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Short-term prediction of secondary progression in a sliding window: A test of a predicting algorithm in a validation cohort

Journal article
Authors Bengt Skoog
Jenny Link
Helen Tedeholm
Marco Longfils
Olle Nerman
Jan Fagius
Oluf Andersen
Published in Multiple Sclerosis Journal - Experimental, Translational and Clinical
Volume 5
Issue 3
Pages 1-10
Publication year 2019
Published at Department of Mathematical Sciences
Institute of Neuroscience and Physiology, Department of Clinical Neuroscience
Pages 1-10
Language en
Links https://doi.org/10.1177/20552173198...
Keywords Epidemiology, multiple sclerosis, outcome measurement, progressive, relapsing/remitting
Subject categories Neurology

Abstract

Introduction: The Multiple Sclerosis Prediction Score (MSPS, www.msprediction.com) estimates, for any month during the course of relapsing–remitting multiple sclerosis (MS), the individual risk of transition to secondary progression (SP) during the following year. Objective: Internal verification of the MSPS algorithm in a derivation cohort, the Gothenburg Incidence Cohort (GIC, n¼ 144) and external verification in the Uppsala MS cohort (UMS, n¼145). Methods: Starting from their second relapse, patients were included and followed for 25 years. A matrix of MSPS values was created. From this matrix, a goodness-of-fit test and suitable diagnostic plots were derived to compare MSPS-calculated and observed outcomes (i.e. transition to SP). Results: The median time to SP was slightly longer in the UMS than in the GIC, 15 vs. 11.5 years (p¼0.19). The MSPS was calibrated with multiplicative factors: 0.599 for the UMS and 0.829 for the GIC; the calibrated MSPS provided a good fit between expected and observed outcomes (chi-square p¼0.61 for the UMS), which indicated the model was not rejected. Conclusion: The results suggest that the MSPS has clinically relevant generalizability in new cohorts, provided that the MSPS was calibrated to the actual overall SP incidence in the cohort.

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